The influence of network structure in neuronal information transmission

Giacomo Baggio
Università di Padova

Understanding how neurons communicate is one of the most challenging open problems in neuroscience. In this talk, I will present some recent results aiming at formulating this problem from a mathematical and information-theoretic viewpoint. After a brief overview on neuronal network dynamical models, I will introduce a digital communication framework for studying the information transmission problem in a neuronal network driven by linear dynamics. Within this framework, a novel metric for measuring the information capacity of a neuronal network based on Shannon’s capacity and the notion of inter-symbol interference will be discussed. Finally, I will illustrate how the structure of the network matrix and, in particular, its departure from normality, affects the information capacity of the network.

Bio-sketch:
Giacomo Baggio received the Laurea Magistrale degree cum laude in Automation Engineering from the University of Padova, Italy, in 2014. He is currently a Ph.D. student at the Department of Information Engineering of the University of Padova. From October 2015 to June 2016, he spent a visiting period in the Control Group at the Department of Engineering, University of Cambridge, UK. His research interests are in the areas of systems theory, stochastic modelling and estimation, and control over networks.